We describe a case of a 30-year-old MSM recently diagnosed with HIV, immunocompromised with a purplish or brown rash all over the body for 3 to 4 months. The histopathology of the cutaneous lesions and pleural effusion aspirate confirmed the diagnosis of Kaposi’s sarcoma (KS) and primary effusion lymphoma (PEL). While KS is one of the AIDS-defining illnesses seen in immunocompromised patients having low CD4 count, PEL is a rare and distinct subset of AIDS-related lymphoma. Despite the widespread availability of HIV testing, HIV diagnosis gets delayed due to stigma among MSM. This case report emphasizes the importance of early suspicion for symptoms of HIV-associated opportunistic infections in high-risk populations like MSM. The report reiterates the need for an ambient stigma-free environment for improving HIV screening in this high-risk population.
Nowadays the leading techniques for diagnosing and
revealing the different diseases are image processing. And there
is an increase in the cases of cancer these days. The unrestricted
development of cells cause’s lumps which leads to brain tumor
also called glioblastoma. There are mainly two types of tumor
benign which has covering over the tumor and malignant is the
one which spreads throughout the places. Earlier the
development of unrestricted cells used to be diagnosed by doctors
physically through monitoring the image by which the results
were not used to be precise sometimes. But time along boarding
of medical fields lead to different medical facilities by which the
results could be precise. The broadly approach method of
imaging that scrutinizes the internal structure of the human race
is Magnetic resonance Imaging. This approach of imaging
techniques is also used for detecting brain tumors. The detection
of glioblastoma processes has machine vision methods such as
Image pre-processing, Segmentation in Image, Feature
extraction and classification. Several image segmentation and
image classification techniques are available for detecting tumor
of the brain. Convolution neural networks (CNN) based
classifiers are proposed to prevail the limitations. This CNN is
such a classifier which is used to differentiate between the
competent data and the trail data, from which the results could
be obtained.
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